비전을 실현하기 위한 5g 무선통신기술 웹...

5G 비전을 실현하기 위한 5G 무선통신기술 웹 세미나

Agilent’s Electronic Measurement Group is now Keysight Technologies.

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Page

Agenda

– Modeling and evaluating multiple waveform techniques

– What do you need for mmWave MIMO radio channel study?

– Multi-Antenna Techniques

© Keysight Technologies 2015 3

Keysight EEsof EDA

Asia Market Development Manager 이 준 부장

Keysight EEsof EDA

Application Engineer 조 성원 부장

Part I. Modeling and evaluating multiple waveform techniques

Keysight EEsof EDA

G Enabling Devices >> Research Challenges


Multi-band • Traditional cellular bands <6GH

• WiFi, BT, GNSS bands

• 5G mmWave bands

Multi-antenna • Impedance matching

• Mutual coupling

• Multi-band, multi-RAT port

sharing

• FD / Massive MIMO

Amplifier • Envelope tracking

• Digital predistortion

• Wide, multi bands

Multiple radio technologies • GSM/EDGE/WCDMA/HSPA/LTE

• WiFi/BT/WiGig/GNSS/5G

Advanced signal processing • Multiple MIMO modes and beamforming

• Network interference suppression

• Adaptive channel estimation / equalization

Full duplex communications • Self interference cancellation

• Dual polarization antenna

• Real time operation

New waveforms • Legacy OFDM enhancement

• FBMC, GFDM, UFMC

Multiple Access • Non-orthogonal

multiple access

• Random / scheduled /

hybrid

• Reference IPs

• Evaluate in multi-domain

• Comparing other technologies

• Prototyping

• Verifying with real hardware

• Unified Software platform

• 1-10Gbps connections to end points

• 1 millisecond delay

• 1000x bandwidth

• 10-100x connected devices

• 99.999% availability

• 100% coverage

• 90% reduction in energy

• 10 year battery life for MTD

Page

Waveform Design Considerations for 5G


Bandwidth /

Frequency

Waveform

New RAT

3GHz 6GHz 30GHz 90GHz

Advanced Multi-Carrier Waveforms1

OFDM FBMC / UFMC / Others Single carrier

>> Wider BW, Higher Fc, much sensitive at phase noise

Note1: • Orthogonal Frequency Division Multiplexing(OFDM)

• Filter Bank Multicarrier(FBMC)

• Universal Filtered Multicarrier(UFMC)

• Generalized Frequency Division Multiplexing(GFDM)

• Frequency Quadrature Amplitude Modulation(FQAM)

OFDMA NOMA SCMA

Page

Waveform Requirements

• Efficiently support high density users

• Optimized multiple access

• Carrier assignment schemes in asynchronous context

• Efficient usage of the allocated spectrum

• Robustness to narrow-band jammers and impulse noise

• High performance spectrum sensing

• Low computational complexity

• Compatibility OFDM vs. NEW


Figure 1.

– OFDM vs. FBMC

Spectrum Using

different filter overlap

factor

Figure 2.

– FBMC Fragmented

Spectrum

Figure 3.

– UFMC multiplex of

sub-bands

Page

OFDM

Advantage

– Good spectral efficiency

– Resistance against multipath interference

– Efficiently implemented using FFTs and IFFTs

– Subcarrier nulls correspond to peaks of

adjacent subcarriers for zero inter-carrier-

interference

Drawback

– Some loss of spectral efficiency due to Cyclic

Prefix insertion

– Imperfect synchronization cause loss of

orthogonality

– Subcarrier intermodulation must be reduced

– High out-of-band power

– Large peak to average power ratio(PAR) leads to

amplifier inefficiency


frequency

f1 f2

Page

Different Type of Waveforms and Filter Operation


^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^

UFMC

OFDM

FBMC

/GFDM

per sub-band

per full-band

per sub-carrier

Page

UFMC - Universal Filtered Multi-Carrier


* OFDM can be implemented by set L as 1

x

+

x

.

.

.

.

.

.

x x

x x

P/S , IFFT Sub-band block

filtering

Figure 1.

Five sub-band multiplexed

Page

GFDM – Generalized Frequency Division Multiplexing


x

+

.

.

.

.

.

.

x

x

Complex

subcarriers

.

.

.

x

Circular convolution implementation in frequency domain

Page

What is Problem Being Solved?


IFF

T

P / S

S / P

FF

T

Sym

bo

l

ma

pp

ing

Sub-c

arr

ier

ma

pp

ing

Sub

-ca

rrie

r

de-m

ap

pin

g

Sym

bo

l

de-m

ap

pin

g

OFDM baseband signal processing blocks

High adjacent channel power ratio(ACPR)

FBMC baseband signal processing blocks

OQ

AM

pre

pro

cessin

g

IFF

T

Po

ly P

ha

se

Ne

two

rk

P / S

S / P

Po

ly P

ha

se

Ne

two

rk

FF

T

OQ

AM

p

ost p

roce

ssin

g

Synthesis Filter bank Analysis Filter bank

Sym

bo

l

ma

pp

ing

Sub-c

arr

ier

ma

pp

ing

Sub

-ca

rrie

r

de-m

ap

pin

g

Sym

bo

l

de-m

ap

pin

g

High peak-to-average power ratio(PAPR)

Page

FBMC Signal Processing Block


Staggering Transform Poly phase

filtering

P/S

Conversion

.

.

.

x

x

x

.

.

.

x

x

x

.

.

.

.

.

.

.

.

.

x +

+ x

x

x

x

x

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

S/P

Conversion

Poly phase

filtering Transform De-

staggering

Sub

channel

processing

OQAM pre-

processing Synthesis Filter Bank Analysis Filter Bank OQAM post-

processing

FBMC transmitter FBMC receiver

Page

OQAM Preprocessing


+

+

x

x

• A time offset of half a QAM symbol period(T/2) is applied to either the real part or the

imaginary part of the QAM symbol

• For two successive sub-channels, say m and m+1, the offset are applied to the real part of

the QAM symbol in sub-channel , while it is applied to the imaginary part of the QAM

symbol in sub-channel m+1.

Page

Synthesis Filter Bank


x +

+ x

x

x

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

* Filter overlap factor K : number of multicarrier symbols which

overlap in the time domain.

* OFDM can be implemented by set K as 1

Page

Sub-channel Equalization


Maximal ratio combined diversity reception

X X

+

X

+

transmitted symbol

Channel

Estimation H[z]

3-tap Complex FIR frequency sampling-design

Evaluation of MRC weighted target values

distorted subcarrier

sequence

Page

End to End Performance Simulation Model


Simulation parameters

ChannelOut

Taps

ModelType=Pedestrian_A

C1 {CommsChannel@Data Flow Models}

• • •• • •

• • • • • •

MAPPER

ModType=QPSK [ModType]M1 {Mapper@Data Flow Models}

1 1 0 1 0

B1 {RandomBits@Data Flow Models}

DeMod IAmp

FreqPhase

Q

FCarrier=1e9 Hz

OutputType=I/Q D3 {Demodulator@Data Flow Models}

• • •• • •

• • • • • •

DEMAPPER

Bits

Node

ModType=QPSK [ModType]D2 {Demapper@Data Flow Models}

FBMC_Source

FBMC_Source_1

Re

Im

C4 {CxToRect@Data Flow Models}

ModOUT

QUADOUT

FreqPhaseQ

IAmp

M2 {Modulator@Data Flow Models}Re

Im

R3 {RectToCx@Data Flow Models}

NoiseDensity

NDensity=10e-12 W [NDensity]

NDensityType=Constant noise density A1 {AddNDensity@Data Flow Models}

FBMC_Receiver

FBMC_Receiver_2

O1 {Oscillator@Data Flow Models}

Random

bit

generation

Symbol

Mapping

FBMC

Reference

Source

LO source

Phase/

Power

Modulator

FO,IQ Im

Wireless

Channel

AWGN

Demodulator

FO,IQ Im

FBMC

Reference

Receiver

BER/FER

Measurem

ent

TEST

REF

BERFER {BER_FER@Data Flow Models}

ADC

Jitter /

Q noise

Page

C++ Models for Faster Simulation


Algorithmic reference to convert

synthesizable fixed point model

Page

Case Study: Cross Domain Modeling & Simulation


IP1dB=8 dBm [TXIFATTIP1dB]

L=6 dB10 [TXIFATTG]

Attn_2 {ATTN_NonLinear}

NF=5 dB [TXIFAMPNF]

G=7 dB [TXIFAMPG]

RFAmp_3 {RFAMP}

Fhi=8500 MHz [FTXIF+1500]

Flo=5500 MHz [FTXIF-1500]

N=3

IL=0.01 dB10

BPF_Butter_3 {BPF_BUTTER}

NF=8.85 dB [TXPANF]

G=20 dB [TXPAG]

RFAmp_4 {RFAMP}

IP1dB=100 dBm [TPAKIP1dB]

L=0 dB10 [TXPAKG+1]

Attn_1 {ATTN_NonLinear}

Fhi=29 GHz

Flo=27 GHz

N=5

IL=0.01 dB10

BPF_Butter_7 {BPF_BUTTER}

ZO=50 Ω

TRANSMITTERROUT {*OUT}

RI

L

LO=7 dBm

ConvGain=0 dB10 [TXRFMIXERG]

Mixer_3 {MIXER_BASIC}

Pwr=7 dBm

F=21000 MHz [3*FTXIF]

LO4 {PwrOscillator}

Source1=Wide: FTXIF MHz at -12 dBm, BW: 1000 MHz

PORT=1

TX_IF_IN {MultiSource}

Baseband Time Domain

Re

Im

C4 {CxToRect@Data Flow Models}

Power=0.01 W

Frequency=7e+9 Hz [FTXIF*1e6]

O1 {Oscillator@Data Flow Models}

ModOUT

QUAD

OUT

Freq

Phase

Q

I

Amp

FCarrier=7e+9 Hz [FTXIF*1e6]

InputType=I/Q

M2 {Modulator@Data Flow Models}

Spectrum Analyzer

ResBW=10000 Hz [RBW]

Start=0 s

Mode=ResBW

SPECTRUM_BB {SpectrumAnalyzerEnv@Data Flow Models}

RF_Link

SYS

CalcPhaseNoise=NO

EnableNoise=NO

FreqSweepSetup=Automatic

Schematic=TX_RF

Subnetwork1 {RF_Link@Data Flow Models}

CCDF

Stop=2e-2 s

Start=0 s

Distribution_FBMC {CCDF_Env@Data Flow Models}

• • •• • •

• • • • • •

MAPPER

ModType=64-QAM [P.FBMC.ModType]

M1 {Mapper@Data Flow Models}

1 1 0 1 0

DataPattern=PN15

B2 {DataPattern@Data Flow Models}

Spectrum Analyzer

ResBW=10000 Hz [RBW]

Start=0 s

Mode=ResBW

SPECTRUM_RF {SpectrumAnalyzerEnv@Data Flow Models}

FBMC_Source

ZC_RootIndex2=150

ZC_RootIndex1=350

FilterBankStructure=PPN_IFFT

FilterCoef=(1x4) [1,-0.972,0.707,-0.2…

FilterOverlapFactor=4

PilotEnable=NO [P.FBMC.PilotEnable]

ActiveSubcAlloc=(1x2) [-750,749]

NumSubcarriers=2048 [P.FBMC.NumSubcarriers]

NumDataSyms=24 [P.FBMC.NumDataSyms]

NumPreambleSyms=6 [P.FBMC.NumPreambleSyms]

IdleInterval=0 s [P.FBMC.IdleInterval]

OversampleRatio=Ratio 1

SampleRate=680e+6 Hz [P.FBMC.SampleRate]

FBMC_Source_1 {FBMC_Source@5G Advanced Modem BEL Models}

RF Frequency Domain

Specification:

• Fc = 28GHz

• Fs = 680MHz

• nFFT = 2048

• BW = 500MHz

• Mod = 64QAM

Page

Case Study Continue:


Develop New Clipping Techniques using Realistic RF Models

FBMC Baseband Spectrum FBMC RF Spectrum without PAPR Reduction

Page

Moving from Simulation to Hardware in the loop


RFIC DUT

• Wider BW (63 GHz BW)

• Higher Sampling (160 GSa/s)

BBIQ - RF

RF - BBIQ

M8190A 12 GSa/S Arbitrary

Waveform Generator

M9703A AXIe 12-bit High-Speed

Digitizer/Wideband Digital Receiver

Interleaving to get 4ch @ 3.2 GSa/s

Infiniium 90000 Q-Series Oscilloscope

I

Q

I

Q

SYSTEMVUE

TEST

REF

BERFER {BER_FER@Data Flow Models}

BPSK, QPSK, ..., up to 4096-QAM

8-PSK, 16-PSK, 16-APSK, 32-APSK16-Star QAM, 32-Star-QAM,

and Custom APSK

Data PayloadPreambleIdle

Frame Structure

Spreading CodeGenerator

X

Digital Modem Sourcefor Linear Modulation

DSSS System

Payload_ModType=16-QAM [Payload_ModType]

Preamble_ModType=BPSK [Preamble_ModType]

Decision Device

FeedwardFilter

-

-

FeedbackFilter

Decision Feedback Equalizer

Fast Computation Algorithm

CIR--->DFE coefficients

Digital Modem Receiver

TrackingAlgorithm=LMS

FreqSync_Mode=CIR Corr

FrameSync_Algorithm=DiffCorr

{DigMod_ReceiverL_FastDFE}

Automatic waveform

creation & download

Reference Source

Reference

Receiver

BER/FER Measurement

Custom modem

design

5G Reference

Library

: Replaceable

in C++, .m or

SV DSP

parts formats

Part II. What do you need for mmWave MIMO radio channel study?

Keysight EEsof EDA

Page

MIMO Fading Channel


How much we know this in higher frequency?

time

frequency

Delay spread

• Frequency selectivity

• Coherence bandwidth(Bc)

Doppler spread

• Time selectivity

• Coherence time(Tc)

Angular spread

• Spatial selectivity

• Coherence distance(Dc)

mnmn

stxmnurxmn

mnHstx

mnVstx

HHmnHVmn

VHmnVVmn

T

mnHurx

mnVurxM

m

nsu

tj

rjrj

F

F

aF

FtH

,,

,,

1

0,,

1

0

,,,

,,,

,,,,

,,,,

,,,

,,,

1

,,

2exp

2exp2exp

;

* Tx antenna element s to Rx element u for cluster n

Page

Channel Sounding / Parameter Extraction / Simulation


Reference transmit signal(chirp/pn)

channel

H[z] ∑ CIR

correlation

Channel

impulse

response

Channel sounding

Estimation

algorithms

Channel

parameters

• PDP (Path delay, path loss)

• AOA, AOD

• Doppler shift

Parameters estimation

• Scenario selection

• Network layout

• Antenna parameters

Large/Small scale

parameters

generation

Fading coefficient

generation

• AS AoA/AoD

• PAS

• Doppler spectrum

• Correlation

• Rician K factor

Statistics & modeling

¤ Input signal faded signal

SystemVue Simulation

SAGE

Maximum likelihood

estimation algorithm

No limitation for number

of path, suitable for both

LOS and NLOS scenarios

Can estimate all the

channel parameters

including path loss and path

delay of each path

Iteration needed, large

computing amount

ESPRIT

Subspace based algorithm

Maximum estimating

number of path is limited by

number of Rx, will be fail

under NLOS scenario

cannot estimate path loss

and path delay

small computing amount

Page

Requirements and Challenges

System Requirements

– Spatial consistency and mobility

– Diffuse versus specular scattering

– Very large antenna arrays

– Frequency range

– Complexity vs. Accuracy

– Applicability of the existing and proposed

models on the 5G requirements

Technical Challenges

– Channel measurement methodology

– High frequency instrumentation

– Ultra-broad band signal

– Synchronization and calibration

– Data streaming

– Channel parameter estimation process


Page

Proposed Architecture / System Considerations


Wideband Arbitrary

Waveform Generator

mmWave Vector Signal

Generator

Wideband Multi-channel

Digitizer

mmWave Multi-channel

Downconverter

mmWave Switch

mmWave

Signal

Wideband I/Q

mmWave

Sounding

Signal

IF Signal

LO: Precision Rubidium Clock

External

AWG

Acquisition

Trigger

Multi-channel Calibration

Power Calibration

System Impulse Response Calibration

Antenna Calibration

I/Q mismatch

correction

LO: Precision Rubidium Clock

Sounding technique • Sliding correlator

• Swept frequency

• Wideband correlation

MIMO capability • Switching @ Tx & Rx

• Parallel Rx & Rx

• Switching @ Tx, parallel Rx

Page

Data Capture and Streaming Considerations


t1 t2

Note: • 1GSa/s sampling rate,

• Tp = transmitting period, Ts = transmitting signal length, Td = max delay spread

• PCI express 2.0 bandwidth(x16 lane) = 64Gbit/s(8GB/s)

Tp = 100 us

Ts = 20 us Td = 5 us

Td = 5 us

Effective Raw Data

Raw Sounding Data

Effective CIR Data

Tp = 100 us

Ts = 20 us Td = 5 us

Effective Raw Data

t3

Td = 5 us

CIR: Channel Impulse Response

Effective CIR Data

32GB/s

8GB/s

1.6GB/s

Reference Signal ¤ Real time implementation

Page

Prototyping and Testing in Real Time Hardware


FPGA

ARRAY M9703

REAL-TIME PROCESSING

Up to 40 Channels x 1GHz wide

CUSTOM

ALGORITHMS

FPGA

ARRAY

– Move forward from largely theoretical massive MIMO research to real hardware

implementation and test

– Open FPGA and download custom algorithms for MIMO and Beamforming

– Test and measure in real-time (ex: channel sounding)

Page

Parameter Estimation Algorithms


Algorithm Consistency Coherent

Signals

Estimation

Performance Computations

Max Num. of

Path

Beamforming

Based

Bartlett L=1 No Poor 1-D search

Capon No No Poor 1-D search

Subspace

Based

MUSIC1 Yes

No;

Yes for ROOT-

MUSIC

Good EVD,

1-D search < Num. of Rx

ESPRIT2

Yes

No;

Yes for TLS-

ESPRIT and

Unitary-ESPRIT

Good EVD < Num. of Rx

ML Based SAGE3

Yes Yes Good

Iterative,

1-D search No limitation

1MUSIC: MUltiple SIgnal Classification 2ESPRIT: Estimating Signal Parameter via Rotation Invariance 3SAGE: Space-Alternating Generalized Expectation maximization 4EVD: Eigen-Value Decomposition

Page

Case Study: Interact between Ray Tracing Fading Engine

– Stochastic channel models fail to accurately represent real-world environments

– Idea: replicate real-world scenes in lab. The scenes originate from

– Measurements (sounder, scanner, UE);

– Ray-tracing simulation software integration.

– System model


2x2 transmitter 2x2 receiver

Custom ray tracing simulation software

Part III. Multi-Antenna Techniques

Keysight EEsof EDA

Page

Motivation

– Higher requirement for system capacity and spectral efficiency(bits/s/Hz)

– To overcome traditional approaches ( expand bandwidth, higher modulation order,

multiple access)

– The MIMO for better use the spatial resource

• The capacity is increased by a multiplication of the number of antennas


MsbitN

SBC

/1log2

Page

Classification


Spatial diversity

Improve robustness

Transmit Diversity Receive Diversity

Space-time block coding (STBC)

X1, X2

-X2, X1*

y1, y2

Spatial division multiplexing

Transmit Beamforming

Spatial multiplexing

Improve user throughput

MIMO

Matrix

X1

X2

y1

y2

Spatial Expansion

Multi-user MIMO

Multi-user Increase system

efficiency

Multi streams/users

.

.

.

.

.

. M a

nte

nn

as

K te

rmin

als

S s

tre

am

s

Massive MIMO

M >> K >> 1

Massive multi-users

Use spatial channel

information? • Open-loop MIMO

• Closed-loop MIMO

Page

Transmit Diversity

– Use transmit diversity to diminish the effects of fading by

transmitting the same information from two different

antennas

– The data from the second antenna is encoded differently

to distinguish it from the primary antenna

– The transmit diversity feature uses ST(space-time) or

SF(space-frequency) block encoding to differentiate the

signals between Antenna 1 and Antenna 2

– The user equipment (UE) must be able to recognize that

the information is coming from two different locations and

properly decode the data.


X1, X2

-X2, X1*

y1, y2

** 12

21

xx

xx

f1 f2

t1 t2

Tx0

Tx1

SFBC:

STBC:

* complex conjugate

Page

Spatial Multiplexing

– Operation Concept

• Transmission of multiple spatial data streams over

different antennas in the same RB

• The dimension of spatial channels is increased and

system capacity increased

– Relevant signal processing

• Perform Layer mapping and Pre-coding to lower the

receiver complexity and reduce the signal interference

between antennas

• Statistic correlation between vector(h11,h12) and

vector(h21,h22 )


X1

X2

y1

y2

h11

h21

h12

h22

x: transmitted signal,

y: received signal,

H: spatial channel matrix,

Hij: channel coefficient from the jth transmit

antenna and the ith receive antenna.

y=Hx

y1=h11x1+h12x2+n1

y2=h21x1+h22x2+n2

Page

Modeling and Simulation for MIMO

– MIMO Tx/Rx simulation under Rayleigh fading and AWGN channel

– Explore different decoding algorithms and performance evaluation

• ML, MMSE-SIC, ZF-SIC, MMSE-Linear, ZF-Linear


A4 {Add@Data Flow Models}

StdDev=707.1e-6 V [StdDev]I2 {IID_Gaussian@Data Flow Models}

StdDev=707.1e-6 V [StdDev]I6 {IID_Gaussian@Data Flow Models}

Re

Im


[ ]

Format=ColumnMajor NumCols=1 [RxNumCols]

NumRows=2 [RxNumRows]P2 {Pack_M@Data Flow Models}

[ ]

Format=ColumnMajor NumCols=1 [TxNumCols]

NumRows=2 [TxNumRows]

U1 {Unpack_M@Data Flow Models}

MIMO_DecoderRec ov eredData

M odType

ChannelRes ponse

Rec eiv edData

DebugFlag=0

ModType=QPSK [ModType]DecoderMethod=ML [DecoderMethod]

Mode=Spatial Multiplexing [Mode]M3 {MIMO_Decoder@5G Advanced Modem Models}

• • •• • •

• • • • • •

DEMAPPER

Bits

Node

ModType=QPSK [ModType]D1 {Demapper@Data Flow Models}

M2 {Mpy@Data Flow Models}

StdDev=0.707 V [1/sqrt(2)]I5 {IID_Gaussian@Data Flow Models}

Re

Im


[ ]

Format=ColumnMajor

NumCols=2 [ChannelNumCols]NumRows=2 [ChannelNumRows]

P1 {Pack_M@Data Flow Models}

StdDev=0.707 V [1/sqrt(2)]I7 {IID_Gaussian@Data Flow Models}

MIMO_Encoder

NumTx=2 [NumTx]Mode=Spatial Multiplexing [Mode]

M1 {MIMO_Encoder@5G Advanced Modem Models}

[ ]

Format=ColumnMajor NumCols=1 [TxNumCols]

NumRows=2 [TxNumRows]P3 {Pack_M@Data Flow Models}

• • •• • •

• • • • • •

MAPPER

ModType=QPSK [ModType]

M5 {Mapper@Data Flow Models}

1 1 0 1 0

B2 {RandomBits@Data Flow Models}

Fading Channel AWGN

Transmit with MIMO coding MIMO decoding and demapper

Page

Multi-User MIMO


Received signal at UE k:

The challenge for MU-MIMO is to find orthogonal

users and design precoding W to minimize the

second term with the restrictions of user grouping,

power, latency and complexity

Hk: kth user’s channel, Wk: weight vector, Sk: data symbol

Capacity Comparison

MU-MIMO Scenario

Page

Modeling and Simulation for Capacity Estimation


Simulation condition

– Transmit antenna number (M) : 4

– Total number of user : from 4 to 100

– SNR=10dB

– Power allocation by waterfilling algorithm

User Scheduler

Power_Selected

W_Selected

H_Selected

H

TotalPower=10 [SNR]

NumRx=1

NumTx=4 [NumTx]

TotalUsers=100 [TotalUsers]

UserScheduler {MATLAB_Script@Data Flow Models}

Channel Capacity

R

P

W

H

NumRx=1

Noise=1

NumTx=4 [NumTx]

SumRate {MATLAB_Script@Data Flow Models}

NumInputsToAverage=100

A1 {Average@Data Flow Models}

123

StartStopOption=Samples

S4 {Sink@Data Flow Models}

StdDev=0.707 V [1/sqrt(2)]

I1 {IID_Gaussian@Data Flow Models}

StdDev=0.707 V [1/sqrt(2)]

I3 {IID_Gaussian@Data Flow Models}

Re

Im


[ ]

Format=ColumnMajor

NumCols=4 [NumTx]

NumRows=1 [NumRx]P4 {Pack_M@Data Flow Models}

BlockSize=1

D2 {Distributor@Data Flow Models}

Channel transfer matrix User scheduling Capacity measurement

User K: 4->100

Su

m C

ap

acity

Page

Massive MIMO

– The use of a very large number of service antennas operated fully

coherent and adaptive

– Brings huge improvements in throughput and energy efficiency

when combined with simultaneous scheduling of a large number of

UEs

– System Model : M transmit antenna with maximum S streams, K

users each with a single antenna

– Originally envisioned for time division duplex(TDD1), but can

potentially be applied in frequency division duplex(FDD)


.

.

.

.

.

. M a

nte

nn

as

K te

rmin

als

S s

tre

am

s

Massive MIMO

M >> K >> 1

Massive multi-users

Note1 : Prefer TDD as not enough resources for pilots and CSI feedback.

Page

Massive MIMO Operation and Challenges

Operation

– Acquire Channel State Information from uplink

Pilots / Data

– Reciprocity calibration and adjustment

– Pre-coding1 to support multi-stream

transmission

– MMSE receiver with beamforming

• Maximum ratio combining(MRC) : interference

and noise are both white in the space

• Interference rejection combining(IRC): colored

interference

Challenges

– Pilot contamination: interference from other cells

• Blind channel estimation?

• Coordination and planning?

– New pre-coder with low-complexity, low-PAPR

– Hardware performance

• I/Q imbalance, A/D resolution, PA linearity

• Phase noise, clock distribution

– Synchronization at low SNR


Note1 : Linear pre-coding [maximum ratio transmission(MRT), zero-forcing(ZF)].

Non-linear pre-coding [Dirty paper coding(DPC)], full CSI required

Page

Modeling and Simulation for Large Number of Antennas


quad_output

output

LO

inputMultiChannel

Modulator

ShowIQ_Impairments=NO MirrorSignal=NO

ConjugatedQuadrature=NO AmpSensitivity=1 [[1]]

InitialPhase=0 ° [[0]]FCarrier=1e6 Hz

NumChannels=1 M1 {MultiCh_Modulator@5G Advanced Modem Models}

TxBeamformer

weights

output

InPhi

InTheta

input

Phi=0 °Theta=0 °

Dy=0.5 Dx=0.5

NumOfAnty=4 NumOfAntx=4

BeamformingType=Calculate by antenna … T1 {Tx_Beamformer@5G Advanced Modem Models}

Env

OutputFc=Center

M4 {MultiCh_AddEnv@5G Advanced Modem Models}

MultiChNoise Density

NDensity=0.0 WNDensityType=Constant noise density

M6 {MultiCh_AddNDensity@5G Advanced Modem Models}

MultiChannel

Demodulator

ShowIQ_Impairments=NO MirrorSignal=NO

AmpSensitivity=1 [[1]]InitialPhase=0 ° [[0]]

FCarrier=1e6 HzNumChannels=1

M2 {MultiCh_Demodulator@5G Advanced Modem Models}

RxBeamformer

weights

output

ref

input

BlockSize=1024 ABF_Algorithm=Sample Matrix Inversion

NumOfTxAnts=16 R1 {Rx_Beamformer@5G Advanced Modem Models}

Power=.010 WFrequency=1000000 HzO1 {Oscillator@Data Flow Models}

Transmit

Beamformer

Multi-CH

Modulator

Multi-CH

Envelope Adder

Multi-CH

AWGN Multi-CH

De-Modulator

Receive

Beamformer

Page

Summary: What you need for your 5G research is…


Transition naturally from Design to Test with a single “cockpit”

Unified

Platform

Software

Quickly capture “system level”

design concepts

Model implementation-level

impairments

Connect BB, RF, and T&M

for rapid validation

Rapid prototyping with

integrated measurement

RF / Analog

Channel Modeling MIMO Channel (OTA)

Digital Pre-Distortion (DPD)

RF System Design

Test Equipment RF Sources & Analyzers

AWG & Digitizers

Scopes, Logic, Modular

Test Software I/O Lib, ComExpert

89600 VSA

Signal Studio

3rd Party

BB Algorithm

Modeling MATLAB .m

FixedPoint, HDL/FPGA

Embedded C++

Filtering, EQ, Modem

IP Reference Libraries 4G LTE-Advanced, LTE ,5G

3G HSPA+, WCDMA, EDGE, GSM

WLAN 802.11ac/n/a/b/g

WPAN 802.11ad, 802.15.3c

RF EDA

Platforms

Model Based Design

Mixed Simulation

Technologies

5G Reference IP

Page

Thank you!

– Resources

• SystemVue : www.keysight.com/find/eesof-systemvue

• 5G Library: www.keysight.com/find/eesof-systemvue-5g-exploration

–Try SystemVue! Obtain a “FREE” 45-day evaluation copy of SystemVue and

explore how SystemVue can help with early 5G systems exploration and evaluation

• http://www.keysight.com/find/eesof-systemvue-evaluation


http://www.keysight.com/find/eesof-systemvue



http://www.keysight.com/find/eesof-systemvue-5g-exploration







http://www.keysight.com/find/eesof-systemvue-evaluation





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